

from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.dm.dm_transit_standby_over_volume_detail_dt import jms_dm__dm_transit_standby_over_volume_detail_dt
from jms.time_sensor.time_after_05_45 import time_after_05_45

jms_dm__dm_transit_standby_over_volume_sum_day_dt = SparkSqlOperator(
    task_id='jms_dm__dm_transit_standby_over_volume_sum_day_dt',
    task_concurrency=1,
    pool_slots=4,
    master='yarn',
    #execution_timeout=timedelta(hours=1)
    #excel平均时长:2分33秒
    #execution_timeout = timedelta(minutes=15)
    #excel平均时长:2分33秒
    execution_timeout = timedelta(minutes=30),
    email='jokic.wang@jtexpress.com',
    name='jms_dm__dm_transit_standby_over_volume_sum_day_dt_{{ execution_date | cst_ds }}',
    sql='jms/dm/dm_transit_standby_over_volume_sum_day_dt/execute.sql',
    driver_memory='3G' , 
    driver_cores=2 , 
    executor_cores=2 , 
    executor_memory='2G' , 
    num_executors=2 , 
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
        'spark.dynamicAllocation.maxExecutors'             : 2 , 
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 180,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.sql.shuffle.partitions': 600,
          'spark.hadoop.hive.exec.dynamic.partition.mode': 'true',
          'spark.yarn.executor.memoryOverhead': 4096,
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 200,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 200,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
)


jms_dm__dm_transit_standby_over_volume_sum_day_dt << [
    jms_dm__dm_transit_standby_over_volume_detail_dt

,time_after_05_45
]
